Lead Data Scientist, Causal Inference, Clinical Outcomes

🕒 May 15

🗽 New York – Remote

🗽 New York – Remote

💵 $100 - $160 / hour

⏳ Contract/Temporary

🟠 Senior

📊 Data Scientist

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Phamily

WebsiteLinkedInAll Job Openings

11 - 50 employees

⚕️ Healthcare Insurance

☁️ SaaS

Healthcare Insurance • SaaS

Phamily is an industry-leading in-house Chronic Care Management (CCM) and Proactive Care platform. The company supports health systems and medical groups nationwide by launching scalable in-house CCM service lines. This enables proactive patient support between visits and delivers sustainable monthly reimbursement without the need for outsourcing. Phamily is known for its cutting-edge technology that improves patient wellness, delivers superior preventative care, and maximizes organizational sustainability. The platform documents care management seamlessly and provides smart care plan templates. Catering to 20+ specialties across 33 states, Phamily enhances patient experience with high engagement rates and reduces hospitalizations significantly. With Phamily, organizations can manage 500 patients per full-time clinical worker, generating substantial revenue and profit.

📋 Description

• Study Design & Execution: Lead a longitudinal, quasi-experimental study starting May 7 to measure clinical outcomes, specifically hospitalization frequency and discharge follow-ups. • Causal Inference Modeling: Apply advanced methodologies (e.g., propensity score matching) to observational data to estimate counterfactual patient outcomes with minimal bias. • Actuarial-Grade Validation: Develop and refine statistical models that will be thoroughly vetted and approved by customer actuaries. • Stakeholder Management: Serve as the primary analytical face to SCMG, gathering requirements and aligning on clinical/business definitions of success. • Data Storytelling: Translate complex statistical findings into compelling presentations and client-ready reports for both technical and non-technical leadership. • Vendor Audit & Wrap-up: Extract usable value from an existing outsourced study, close out the vendor contract, and integrate relevant findings into the final study. • Technical Infrastructure: Navigate and build analytics reporting infrastructure using SQL, Python, dbt, Redshift, and Looker. • Project Handoff: Ensure all code is clean and reproducible for final handover to the internal Phamily BI team.

🎯 Requirements

• Health-Tech Expertise: Deep experience in causal inference, metric design, and clinical outcomes evaluation. • Data Proficiency: Extensive experience working with complex EHR and healthcare claims data. • Advanced Analytics Toolkit: Highly capable in Python, R, SQL, dbt, Redshift, and Looker. • Statistical Matching: Proven experience developing algorithms for high-dimensional statistical matching with large datasets. • Security Standards: Practical experience maintaining strict PHI security protocols while building data infrastructure. • Analytical Rigor: Ability to design and execute 'actuarial-grade' studies that control for significant confounding variables. • Communication: Exceptional ability to synthesize technical data into narratives for non-technical clients. • Education: Advanced degree in a quantitative field (e.g., Data Science, Statistics, Health Economics, or Epidemiology).

🏖️ Benefits

• Competitive compensation commensurate with experience • Potential to earn equity based on performance • Medical, dental, and vision coverage for employees and dependents at a nominal cost • Paid maternity leave • FSA and Dependent Care account options • 401(k) Eligibility after 6 months of full-time employment • Collaborative, mission-driven work environment